sensitivity: Does the solution survive a different seed?

In latents: Multilevel Latent Profile, Class and Transition Models

Does the solution survive a different seed?

Description

A latent profile model is fitted by EM from random starts, so what comes back is a local maximum reached from one particular set of them. n_starts reports how many starts within a single seed's stream reached the best likelihood; it cannot say whether a different stream would have found a different mode. This verb refits the model under each of several seeds and reports what changed.

Usage

sensitivity(
  x,
  data = NULL,
  seeds = 1:10,
  n_starts = NULL,
  max_iter = NULL,
  tol = NULL,
  tolerance = NULL
)

Arguments

x	A fitted multilpa model to use as the reference. A fit_staged() result varies the second-stage starts while holding its first-stage measurement solution fixed. A directly fitted model with measurement blocks held is refused: its original free starting values are not retained, so replaying the same seed would not replay the same search.
data	Optional. The data the model was fitted to, in its original row order. A fit carries the columns it was built from, so this is only needed to override them. Shared identifier and indicator columns are checked against the fit before assignment agreement is calculated.
seeds	Seeds to refit under: at least two distinct whole numbers of either sign, as set.seed() accepts. Anything else raises latents_bad_argument. The reference fit's own seed may be among them, in which case that row reproduces it and is the arithmetic check that the refit is the same model.
n_starts	Starts per refit. Defaults to the number the reference fit used, so each seed gets the same search effort it did.
max_iter, tol	Passed to each refit; default to the reference fit's.
tolerance	Two log likelihoods within this of each other count as the same optimum. Defaults to the reference fit's own replication tolerance, which is what it used to decide whether its starts agreed.

Details

Three things are worth reading off the result. Whether every seed reached the same maximised log likelihood, which is what optimum counts. Whether the seeds that reached it converged. And how many observations were assigned to a different profile, which is what agreement measures, after the arbitrary profile labels of each refit have been matched to the reference fit's – two fits of the same mixture can be identical and still number their profiles differently, so comparing labels directly would report disagreement that is not there.

A seed whose refit fails contributes a row with NA estimates rather than being dropped silently, and a latents_sensitivity_dropped warning names how many failed, so the table is never quietly shorter than seeds.

Value

A base data.frame, one row per seed, with columns seed, log_likelihood, converged, iterations, optimum (1 for the best maximum found across the seeds, 2 for the next distinct one, and so on), best (whether this seed reached optimum 1) and agreement (the proportion of observations given the same profile as the reference fit, after label alignment). A failed refit has NA throughout except seed.

References

Hipp, J. R., & Bauer, D. J. (2006). Local solutions in the estimation of growth mixture models. Psychological Methods, 11, 36–53.

See Also

enumerate_classes() to vary the number of classes rather than the seed, and parameter_inference() for uncertainty about the parameters of a single solution.

Examples

set.seed(7)
example_data <- data.frame(
  school = rep(seq_len(12), each = 10),
  score_a = c(rnorm(60, -1.5), rnorm(60, 1.5)),
  score_b = c(rnorm(60, -1), rnorm(60, 1))
)
fit <- multilpa(example_data, c("score_a", "score_b"), "school",
                n_profiles = 2, n_group_classes = 1, n_starts = 3, seed = 1)
sensitivity(fit, seeds = 1:3, n_starts = 3)

latents documentation built on Oct. 16, 2026, 5:09 p.m.